Abstract:

The Phase I effort will include a detailed study design leading to the identification of intelligent tutor dimensions, components, and instructional features that are impacted by student model grain size. The study will also develop the experimentalmethodology that will compare learning effectiveness and instructional efficiency information for tutor variations of different grain size. In addition, costs for design and development of the fine-grain models and their courser-grain derivative will berecovered and analyzed as a basis for comparison.Sonalysts proposes ExpertTrainO simulation-based intelligent tutoring technology as the basis for the grain size comparison. As we will describe, the ExpertTrain engine readily accommodates moderate to course grain sizes and can be extended to supportcognitive diagnosis. Sonalysts has produced a number of fielded tutors with the ExpertTrain engine. Considering technical issues, experimental design, costs, and commercialization potential, we will, in conjunction with the sponsor, determine whether anyof these courses can be used as the basis for the comparison, or whether a new, albeit limited, course should be developed.Phase II will include completion of multiple tutors (varying in grain-size) and performance of a comparative analysis. This research will significantly contribute to development of cost versus effectiveness metrics for various intelligent tutor componentsand features, particularly those related to grain size. We anticipate that the most useful metrics will describe the curve shapes for tutor cost and training/ remediation time versus granularity. The shape of these curves coupled with standard trainingplanning factors (e.g., student throughput, technology refresh cycle, etc.) will help resource sponsors and program managers make reliable training investment decisions based on life-cycle and total ownership costs. Reliable cost versus benefit metricsare key decision-making tools for programs such as DD 21, JSF, Space Based Infrared Systems.